{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI\n",
    "from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent\n",
    "from langchain_experimental.agents import create_pandas_dataframe_agent\n",
    "from langchain_community.chat_models import ChatOpenAI\n",
    "import pandas as pd\n",
    "import openai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(api_key: str, prompt: str, local_file_path: str) -> str:\n",
    "    print(api_key)\n",
    "    # df = pd.read_csv(local_file_path, delimiter=\",\", quotechar='\"')\n",
    "    df = pd.read_csv(local_file_path)\n",
    "    df = df.iloc[0:]  # Skip the first row\n",
    "    # df['CrashId'].value_counts().idxmax()\n",
    "\n",
    "    agent = create_pandas_dataframe_agent(ChatOpenAI(temperature=0, openai_api_key=api_key, model_name=\"gpt-3.5-turbo\"), df, verbose=True)\n",
    "\n",
    "    response = agent.run(prompt)\n",
    "    print(response)\n",
    "\n",
    "    return response\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "openai.api_key = 'apikey'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = 'apikey'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"What is the most frequent place of crash, could you make a histogram to show the 5 most frequent places of car crash?\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_file_path=\"data/export_175286_0.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: To find the most frequent place of crash, we need to analyze the 'Town Name' column in the dataframe.\n",
      "Action: Use value_counts() to count the occurrences of each town name.\n",
      "Action Input: df['Town Name'].value_counts()\u001b[0mUse value_counts() to count the occurrences of each town name. is not a valid tool, try one of [python_repl_ast].\u001b[32;1m\u001b[1;3mI should use python_repl_ast to count the occurrences of each town name in the 'Town Name' column.\n",
      "Action: python_repl_ast\n",
      "Action Input: df['Town Name'].value_counts()\u001b[0m\u001b[36;1m\u001b[1;3mTown Name\n",
      "New Haven        304\n",
      "Bridgeport       149\n",
      "Hartford         107\n",
      "Stamford          99\n",
      "Waterbury         86\n",
      "                ... \n",
      "Wilton             1\n",
      "East Windsor       1\n",
      "Rocky Hill         1\n",
      "Seymour            1\n",
      "Windsor Locks      1\n",
      "Name: count, Length: 115, dtype: int64\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer. I will create a histogram to show the 5 most frequent places of car crash, which are New Haven, Bridgeport, Hartford, Stamford, and Waterbury.\n",
      "Final Answer: The 5 most frequent places of car crash are New Haven, Bridgeport, Hartford, Stamford, and Waterbury.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "The 5 most frequent places of car crash are New Haven, Bridgeport, Hartford, Stamford, and Waterbury.\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "response = run(api_key, prompt, local_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"What is the most frequent place of crash, could you plot a pie chart to show the 5 most frequent places of car crash?\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: To answer this question, I need to find the column that represents the place of crash. Looking at the dataframe, the column \"Name Of Roadway On Which Vehicle Was Traveling\" seems to be the one that represents the place of crash. I will first check the most frequent places of crash by using the value_counts() function on this column. Then, I will plot a pie chart of the 5 most frequent places of crash. \n",
      "\n",
      "Action: python_repl_ast\n",
      "Action Input: df['Name Of Roadway On Which Vehicle Was Traveling'].value_counts().head(5)\u001b[0m\u001b[36;1m\u001b[1;3mName Of Roadway On Which Vehicle Was Traveling\n",
      "Main Street    31\n",
      "Whalley Ave    25\n",
      "Parking lot    24\n",
      "Chapel St      21\n",
      "Parking Lot    17\n",
      "Name: count, dtype: int64\u001b[0m\u001b[32;1m\u001b[1;3mThe most frequent places of crash are Main Street, Whalley Ave, Parking lot, Chapel St, and Parking Lot. Now, I will plot a pie chart to visualize these data.\n",
      "\n",
      "Action: python_repl_ast\n",
      "Action Input: df['Name Of Roadway On Which Vehicle Was Traveling'].value_counts().head(5).plot(kind='pie', autopct='%1.1f%%')\u001b[0m\u001b[36;1m\u001b[1;3mAxes(0.22375,0.11;0.5775x0.77)\u001b[0m\u001b[32;1m\u001b[1;3mI have successfully plotted a pie chart showing the 5 most frequent places of car crash. The pie chart shows the percentage of each place in the top 5 places of car crash.\n",
      "Final Answer: The most frequent places of crash are Main Street, Whalley Ave, Parking lot, Chapel St, and Parking Lot. The pie chart has been plotted to visualize these data.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "The most frequent places of crash are Main Street, Whalley Ave, Parking lot, Chapel St, and Parking Lot. The pie chart has been plotted to visualize these data.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response = run(api_key, prompt, local_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yul23028/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In realms of code, where knowledge flows,\n",
      "A marvel rose, with purpose it knows.\n",
      "LangChain, the bard, with words so grand,\n",
      "A tapestry of language, at its command.\n",
      "\n",
      "From endless depths of data's vast sea,\n",
      "It weaves a tale, for all to see.\n",
      "Words dance and flow, like a river's song,\n",
      "Unveiling truths, where meaning belongs.\n",
      "\n",
      "With every query, a symphony takes flight,\n",
      "A dance of words, illuminating the night.\n",
      "From prose to verse, from tales to code,\n",
      "LangChain's melody, a harmonious ode.\n",
      "\n",
      "Through ancient texts and modern day speech,\n",
      "It navigates the labyrinth, each layer it seeks.\n",
      "Connecting thoughts, bridging distant shores,\n",
      "Unveiling patterns, unlocking hidden doors.\n",
      "\n",
      "In classrooms bright, where knowledge takes root,\n",
      "LangChain empowers, with its wisdom's loot.\n",
      "Students marvel, as words come alive,\n",
      "Their understanding, it helps to thrive.\n",
      "\n",
      "In research labs, where innovation thrives,\n",
      "LangChain assists, as knowledge it provides.\n",
      "From scientific papers to medical texts,\n",
      "It unveils insights, where progress connects.\n",
      "\n",
      "But LangChain's power, it goes beyond,\n",
      "In realms of art, its magic is fond.\n",
      "With brushstrokes of code, it paints a vibrant hue,\n",
      "Creating worlds, where dreams come true.\n",
      "\n",
      "Its verse inspires, its words ignite,\n",
      "A muse for poets, a beacon of light.\n",
      "In ancient tales and modern-day prose,\n",
      "LangChain's legacy, forever it grows.\n",
      "\n",
      "So let us sing, of LangChain's might,\n",
      "A ballad of knowledge, a beacon of light.\n",
      "In realms of language, its melody shall soar,\n",
      "A timeless tale, forever to be adored.\n"
     ]
    }
   ],
   "source": [
    "from langchain_google_vertexai import VertexAI\n",
    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
    "google_llm = ChatGoogleGenerativeAI(model=\"gemini-pro\", google_api_key='AIzaSyCyzu9fip155nvvDRkmSq1QPBqssXxdbEU')\n",
    "result = google_llm.invoke(\"Write a ballad about LangChain\")\n",
    "print(result.content)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mThought: First, I need to group the dataframe by `Name Of Roadway On Which Vehicle Was Traveling` and count the number of occurrences. Then I need to sort the dataframe by the number of occurrences in descending order. Finally, I should use the `matplotlib` package to create a pie chart of the 5 most frequent places of car crash.\n",
      "Action: python_repl_ast\n",
      "Action Input: \n",
      "```python\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Group the dataframe by `Name Of Roadway On Which Vehicle Was Traveling` and count the number of occurrences\n",
      "crash_location_counts = df.groupby('Name Of Roadway On Which Vehicle Was Traveling').size().reset_index(name='count')\n",
      "\n",
      "# Sort the dataframe by the number of occurrences in descending order\n",
      "crash_location_counts = crash_location_counts.sort_values('count', ascending=False)\n",
      "\n",
      "# Get the top 5 most frequent places of car crash\n",
      "top_5_crash_locations = crash_location_counts.head(5)\n",
      "\n",
      "# Create a pie chart of the top 5 most frequent places of car crash\n",
      "plt.pie(top_5_crash_locations['count'], labels=top_5_crash_locations['Name Of Roadway On Which Vehicle Was Traveling'], autopct='%1.1f%%')\n",
      "plt.title('Top 5 Most Frequent Places of Car Crash')\n",
      "plt.show()\n",
      "```\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mAction: python_repl_ast\n",
      "Action Input: \n",
      "```python\n",
      "print(top_5_crash_locations)\n",
      "```\u001b[0m\u001b[36;1m\u001b[1;3m     Name Of Roadway On Which Vehicle Was Traveling  count\n",
      "634                                     Main Street     31\n",
      "1168                                    Whalley Ave     25\n",
      "814                                     Parking lot     24\n",
      "231                                       Chapel St     21\n",
      "808                                     Parking Lot     17\n",
      "\u001b[0m"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Parsing LLM output produced both a final answer and a parse-able action:: Action: python_repl_ast\nAction Input: \n```python\n# Create a pie chart of the top 5 most frequent places of car crash\nplt.pie(top_5_crash_locations['count'], labels=top_5_crash_locations['Name Of Roadway On Which Vehicle Was Traveling'], autopct='%1.1f%%')\nplt.title('Top 5 Most Frequent Places of Car Crash')\nplt.show()\n```\nFinal Answer: The five most frequent places for car crashes are:\n1. Main Street - 31\n2. Whalley Ave - 25\n3. Parking lot - 24\n4. Chapel St - 21\n5. Parking Lot - 17",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutputParserException\u001b[0m                     Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:1125\u001b[0m, in \u001b[0;36mAgentExecutor._iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m   1124\u001b[0m     \u001b[38;5;66;03m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m-> 1125\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1128\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1129\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1130\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutputParserException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:387\u001b[0m, in \u001b[0;36mRunnableAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    386\u001b[0m final_output: Any \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunnable\u001b[38;5;241m.\u001b[39mstream(inputs, config\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks}):\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m final_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2427\u001b[0m, in \u001b[0;36mRunnableSequence.stream\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2421\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstream\u001b[39m(\n\u001b[1;32m   2422\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   2423\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   2424\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2425\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   2426\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2427\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(\u001b[38;5;28miter\u001b[39m([\u001b[38;5;28minput\u001b[39m]), config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2414\u001b[0m, in \u001b[0;36mRunnableSequence.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2408\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m   2409\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   2410\u001b[0m     \u001b[38;5;28minput\u001b[39m: Iterator[Input],\n\u001b[1;32m   2411\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   2412\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   2413\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Output]:\n\u001b[0;32m-> 2414\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m   2415\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m   2416\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m   2417\u001b[0m         patch_config(config, run_name\u001b[38;5;241m=\u001b[39m(config \u001b[38;5;129;01mor\u001b[39;00m {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m   2418\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   2419\u001b[0m     )\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1494\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1493\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1494\u001b[0m     chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m   1495\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m chunk\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:2378\u001b[0m, in \u001b[0;36mRunnableSequence._transform\u001b[0;34m(self, input, run_manager, config)\u001b[0m\n\u001b[1;32m   2370\u001b[0m     final_pipeline \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m   2371\u001b[0m         final_pipeline,\n\u001b[1;32m   2372\u001b[0m         patch_config(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2375\u001b[0m         ),\n\u001b[1;32m   2376\u001b[0m     )\n\u001b[0;32m-> 2378\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m final_pipeline:\n\u001b[1;32m   2379\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m output\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1042\u001b[0m, in \u001b[0;36mRunnable.transform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m got_first_val:\n\u001b[0;32m-> 1042\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(final, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:577\u001b[0m, in \u001b[0;36mRunnable.stream\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    574\u001b[0m \u001b[38;5;124;03mDefault implementation of stream, which calls invoke.\u001b[39;00m\n\u001b[1;32m    575\u001b[0m \u001b[38;5;124;03mSubclasses should override this method if they support streaming output.\u001b[39;00m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 577\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/output_parsers/base.py:169\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[0;32m--> 169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minner_input\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/base.py:1243\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1240\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m   1241\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m   1242\u001b[0m         Output,\n\u001b[0;32m-> 1243\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1244\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1245\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1246\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1247\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1248\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1249\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1250\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1251\u001b[0m     )\n\u001b[1;32m   1252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/output_parsers/base.py:170\u001b[0m, in \u001b[0;36mBaseOutputParser.invoke.<locals>.<lambda>\u001b[0;34m(inner_input)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28minput\u001b[39m, BaseMessage):\n\u001b[1;32m    169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_with_config(\n\u001b[0;32m--> 170\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m inner_input: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatGeneration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minner_input\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    173\u001b[0m         \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    174\u001b[0m         config,\n\u001b[1;32m    175\u001b[0m         run_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    176\u001b[0m     )\n\u001b[1;32m    177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/output_parsers/base.py:221\u001b[0m, in \u001b[0;36mBaseOutputParser.parse_result\u001b[0;34m(self, result, partial)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Parse a list of candidate model Generations into a specific format.\u001b[39;00m\n\u001b[1;32m    210\u001b[0m \n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03mThe return value is parsed from only the first Generation in the result, which\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;124;03m    Structured output.\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/output_parsers/react_single_input.py:59\u001b[0m, in \u001b[0;36mReActSingleInputOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m includes_answer:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(\n\u001b[1;32m     60\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mFINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     61\u001b[0m     )\n\u001b[1;32m     62\u001b[0m action \u001b[38;5;241m=\u001b[39m action_match\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mstrip()\n",
      "\u001b[0;31mOutputParserException\u001b[0m: Parsing LLM output produced both a final answer and a parse-able action:: Action: python_repl_ast\nAction Input: \n```python\n# Create a pie chart of the top 5 most frequent places of car crash\nplt.pie(top_5_crash_locations['count'], labels=top_5_crash_locations['Name Of Roadway On Which Vehicle Was Traveling'], autopct='%1.1f%%')\nplt.title('Top 5 Most Frequent Places of Car Crash')\nplt.show()\n```\nFinal Answer: The five most frequent places for car crashes are:\n1. Main Street - 31\n2. Whalley Ave - 25\n3. Parking lot - 24\n4. Chapel St - 21\n5. Parking Lot - 17",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(local_file_path)\n\u001b[1;32m      2\u001b[0m google_agent \u001b[38;5;241m=\u001b[39m create_pandas_dataframe_agent(llm\u001b[38;5;241m=\u001b[39mgoogle_llm, df\u001b[38;5;241m=\u001b[39mdf, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m----> 4\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgoogle_agent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(response)\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/_api/deprecation.py:145\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    144\u001b[0m     emit_warning()\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/chains/base.py:545\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    543\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    544\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 545\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m[\n\u001b[1;32m    546\u001b[0m         _output_key\n\u001b[1;32m    547\u001b[0m     ]\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m    550\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs, callbacks\u001b[38;5;241m=\u001b[39mcallbacks, tags\u001b[38;5;241m=\u001b[39mtags, metadata\u001b[38;5;241m=\u001b[39mmetadata)[\n\u001b[1;32m    551\u001b[0m         _output_key\n\u001b[1;32m    552\u001b[0m     ]\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain_core/_api/deprecation.py:145\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.warning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    144\u001b[0m     emit_warning()\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/chains/base.py:378\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Execute the chain.\u001b[39;00m\n\u001b[1;32m    347\u001b[0m \n\u001b[1;32m    348\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;124;03m        `Chain.output_keys`.\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    371\u001b[0m config \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    372\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m: callbacks,\n\u001b[1;32m    373\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m: tags,\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m: metadata,\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: run_name,\n\u001b[1;32m    376\u001b[0m }\n\u001b[0;32m--> 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mRunnableConfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_only_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m    \u001b[49m\u001b[43minclude_run_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_run_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/chains/base.py:163\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    162\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 163\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    164\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m include_run_info:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/chains/base.py:153\u001b[0m, in \u001b[0;36mChain.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_inputs(inputs)\n\u001b[1;32m    152\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 153\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    154\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    155\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m    156\u001b[0m     )\n\u001b[1;32m    158\u001b[0m     final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m    159\u001b[0m         inputs, outputs, return_only_outputs\n\u001b[1;32m    160\u001b[0m     )\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:1391\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m   1389\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m-> 1391\u001b[0m     next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1392\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1393\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1394\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1395\u001b[0m \u001b[43m        \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1396\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1397\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1398\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m   1399\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(\n\u001b[1;32m   1400\u001b[0m             next_step_output, intermediate_steps, run_manager\u001b[38;5;241m=\u001b[39mrun_manager\n\u001b[1;32m   1401\u001b[0m         )\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:1097\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m   1088\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m   1089\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1090\u001b[0m     name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1094\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1095\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m   1096\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1097\u001b[0m         [\n\u001b[1;32m   1098\u001b[0m             a\n\u001b[1;32m   1099\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m   1100\u001b[0m                 name_to_tool_map,\n\u001b[1;32m   1101\u001b[0m                 color_mapping,\n\u001b[1;32m   1102\u001b[0m                 inputs,\n\u001b[1;32m   1103\u001b[0m                 intermediate_steps,\n\u001b[1;32m   1104\u001b[0m                 run_manager,\n\u001b[1;32m   1105\u001b[0m             )\n\u001b[1;32m   1106\u001b[0m         ]\n\u001b[1;32m   1107\u001b[0m     )\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:1097\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1088\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_next_step\u001b[39m(\n\u001b[1;32m   1089\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1090\u001b[0m     name_to_tool_map: Dict[\u001b[38;5;28mstr\u001b[39m, BaseTool],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1094\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1095\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[AgentFinish, List[Tuple[AgentAction, \u001b[38;5;28mstr\u001b[39m]]]:\n\u001b[1;32m   1096\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consume_next_step(\n\u001b[0;32m-> 1097\u001b[0m         [\n\u001b[1;32m   1098\u001b[0m             a\n\u001b[1;32m   1099\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_next_step(\n\u001b[1;32m   1100\u001b[0m                 name_to_tool_map,\n\u001b[1;32m   1101\u001b[0m                 color_mapping,\n\u001b[1;32m   1102\u001b[0m                 inputs,\n\u001b[1;32m   1103\u001b[0m                 intermediate_steps,\n\u001b[1;32m   1104\u001b[0m                 run_manager,\n\u001b[1;32m   1105\u001b[0m             )\n\u001b[1;32m   1106\u001b[0m         ]\n\u001b[1;32m   1107\u001b[0m     )\n",
      "File \u001b[0;32m~/miniconda3/envs/langchain_llm_venv/lib/python3.9/site-packages/langchain/agents/agent.py:1136\u001b[0m, in \u001b[0;36mAgentExecutor._iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m   1134\u001b[0m     raise_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raise_error:\n\u001b[0;32m-> 1136\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1137\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn output parsing error occurred. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1138\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn order to pass this error back to the agent and have it try \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1139\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magain, pass `handle_parsing_errors=True` to the AgentExecutor. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1140\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis is the error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1141\u001b[0m     )\n\u001b[1;32m   1142\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m   1143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_parsing_errors, \u001b[38;5;28mbool\u001b[39m):\n",
      "\u001b[0;31mValueError\u001b[0m: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Parsing LLM output produced both a final answer and a parse-able action:: Action: python_repl_ast\nAction Input: \n```python\n# Create a pie chart of the top 5 most frequent places of car crash\nplt.pie(top_5_crash_locations['count'], labels=top_5_crash_locations['Name Of Roadway On Which Vehicle Was Traveling'], autopct='%1.1f%%')\nplt.title('Top 5 Most Frequent Places of Car Crash')\nplt.show()\n```\nFinal Answer: The five most frequent places for car crashes are:\n1. Main Street - 31\n2. Whalley Ave - 25\n3. Parking lot - 24\n4. Chapel St - 21\n5. Parking Lot - 17"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(local_file_path)\n",
    "google_agent = create_pandas_dataframe_agent(llm=google_llm, df=df, verbose=True)\n",
    "\n",
    "response = google_agent.run(prompt)\n",
    "print(response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: We can use `df['Trafficway Description'].value_counts()` to get the count of each trafficway description, and then plot the top 5 using matplotlib.\n",
      "Action: python_repl_ast\n",
      "Action Input: import matplotlib.pyplot as plt\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3mAction: python_repl_ast\n",
      "Action Input: df['Trafficway Description'].value_counts().head(5).plot.pie(autopct='%1.1f%%')\n",
      "plt.show()\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mFinal Answer: \n",
      "\n",
      "```\n",
      "import matplotlib.pyplot as plt\n",
      "df['Trafficway Description'].value_counts().head(5).plot.pie(autopct='%1.1f%%')\n",
      "plt.show()\n",
      "```\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "```\n",
      "import matplotlib.pyplot as plt\n",
      "df['Trafficway Description'].value_counts().head(5).plot.pie(autopct='%1.1f%%')\n",
      "plt.show()\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(local_file_path)\n",
    "google_agent = create_pandas_dataframe_agent(llm=google_llm, df=df, verbose=True)\n",
    "\n",
    "response = google_agent.run(prompt)\n",
    "print(response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CrashId,\"Fatal', 'Case', 'Status\",\"Fatal', 'Case.1', 'Status', 'Text',\n",
      "       'Format\",\"CrashId\",\"CrashId\",\"Latitude\",\"Longitude\",\"DOT', 'Case.2',\n",
      "       'Number\",\"Town', 'Name\",\"Town',\n",
      "       ...\n",
      "       'Classification\",\"Functional', 'Classification', 'Text.34',\n",
      "       'Format\",\"Rural', 'or', 'Urban\",\"Rural', 'or.1', 'Urban', 'Text.35',\n",
      "       'Format\"'],\n",
      "      dtype='object', length=264)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(local_file_path, delimiter=\" \", quotechar='\"', header=0)\n",
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>https://www.ctcrash.uconn.edu/QueryTool2.action?qid=175286</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CrashId,\"Fatal</th>\n",
       "      <th>Case</th>\n",
       "      <th>Status\",\"Fatal</th>\n",
       "      <th>Case</th>\n",
       "      <th>Status</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"CrashId\",\"CrashId\",\"Latitude\",\"Longitude\",\"DOT</th>\n",
       "      <th>Case</th>\n",
       "      <th>Number\",\"Town</th>\n",
       "      <th>Name\",\"Town</th>\n",
       "      <th>Number\",\"Town</th>\n",
       "      <th>Number\",\"Town</th>\n",
       "      <th>Number</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Date</th>\n",
       "      <th>Of</th>\n",
       "      <th>Crash\",\"Year\",\"Month\",\"Month</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Day\",\"Day</th>\n",
       "      <th>of</th>\n",
       "      <th>the</th>\n",
       "      <th>Week\",\"Day</th>\n",
       "      <th>of</th>\n",
       "      <th>the</th>\n",
       "      <th>Week</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Time</th>\n",
       "      <th>of</th>\n",
       "      <th>Crash\",\"Hour</th>\n",
       "      <th>of</th>\n",
       "      <th>the</th>\n",
       "      <th>Day\",\"Case</th>\n",
       "      <th>Identifier\",\"Crash</th>\n",
       "      <th>Severity\",\"Crash</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Most</th>\n",
       "      <th>Severe</th>\n",
       "      <th>Injury\",\"Most</th>\n",
       "      <th>Severe</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Number</th>\n",
       "      <th>Of</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Vehicles\",\"Number</th>\n",
       "      <th>Of</th>\n",
       "      <th>Non-Motorist\",\"Number</th>\n",
       "      <th>Of</th>\n",
       "      <th>Witnesses\",\"Route</th>\n",
       "      <th>Class\",\"Route</th>\n",
       "      <th>Class</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Milemarker\",\"Road</th>\n",
       "      <th>Number\",\"Road</th>\n",
       "      <th>Description\",\"Roadway</th>\n",
       "      <th>Name\",\"Intersecting</th>\n",
       "      <th>Roadway</th>\n",
       "      <th>Name\",\"Landmark</th>\n",
       "      <th>Description\",\"Distance</th>\n",
       "      <th>From</th>\n",
       "      <th>Nearest</th>\n",
       "      <th>Landmark\",\"Unit</th>\n",
       "      <th>Of</th>\n",
       "      <th>Measure</th>\n",
       "      <th>From</th>\n",
       "      <th>Nearest</th>\n",
       "      <th>Landmark\",\"Direction</th>\n",
       "      <th>From</th>\n",
       "      <th>Nearest</th>\n",
       "      <th>Landmark\",\"Latitude\",\"Longitude\",\"First</th>\n",
       "      <th>Harmful</th>\n",
       "      <th>Event\",\"First</th>\n",
       "      <th>Harmful</th>\n",
       "      <th>Event</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Manner</th>\n",
       "      <th>of</th>\n",
       "      <th>Crash</th>\n",
       "      <th>/</th>\n",
       "      <th>Collision</th>\n",
       "      <th>Impact\",\"Manner</th>\n",
       "      <th>of</th>\n",
       "      <th>Crash</th>\n",
       "      <th>/</th>\n",
       "      <th>Collision</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Location</th>\n",
       "      <th>of</th>\n",
       "      <th>First</th>\n",
       "      <th>Harmful</th>\n",
       "      <th>Event\",\"Location</th>\n",
       "      <th>of</th>\n",
       "      <th>First</th>\n",
       "      <th>Harmful</th>\n",
       "      <th>Event</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Weather</th>\n",
       "      <th>Condition\",\"Weather</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Weather</th>\n",
       "      <th>2\",\"Weather</th>\n",
       "      <th>2.0</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Light</th>\n",
       "      <th>Condition\",\"Light</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Road</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Condition\",\"Road</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Contributing</th>\n",
       "      <th>Circumstances,</th>\n",
       "      <th>Environment\",\"Contributing</th>\n",
       "      <th>Circumstances,</th>\n",
       "      <th>Environment</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Environment</th>\n",
       "      <th>2\",\"Environment</th>\n",
       "      <th>2.0</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Environment</th>\n",
       "      <th>3\",\"Environment</th>\n",
       "      <th>3.0</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Contributing</th>\n",
       "      <th>Circumstances,</th>\n",
       "      <th>Road\",\"Contributing</th>\n",
       "      <th>Circumstances,</th>\n",
       "      <th>Road</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Road</th>\n",
       "      <th>2\",\"Road</th>\n",
       "      <th>2.0</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Road</th>\n",
       "      <th>3\",\"Road</th>\n",
       "      <th>3.0</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Crash</th>\n",
       "      <th>Specific</th>\n",
       "      <th>Location\",\"Crash</th>\n",
       "      <th>Specific</th>\n",
       "      <th>Location</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Type</th>\n",
       "      <th>of</th>\n",
       "      <th>Intersection\",\"Type</th>\n",
       "      <th>of</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"School</th>\n",
       "      <th>Bus</th>\n",
       "      <th>Related\",\"School</th>\n",
       "      <th>Bus</th>\n",
       "      <th>Related</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Work</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Related\",\"Work</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Related</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Location</th>\n",
       "      <th>Of</th>\n",
       "      <th>Crash</th>\n",
       "      <th>Relative</th>\n",
       "      <th>To</th>\n",
       "      <th>Work</th>\n",
       "      <th>Zone\",\"Location</th>\n",
       "      <th>Of</th>\n",
       "      <th>Crash</th>\n",
       "      <th>Relative</th>\n",
       "      <th>To</th>\n",
       "      <th>Work</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Type</th>\n",
       "      <th>of</th>\n",
       "      <th>Work</th>\n",
       "      <th>Zone\",\"Type</th>\n",
       "      <th>of</th>\n",
       "      <th>Work</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Worker</th>\n",
       "      <th>Presence\",\"Worker</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Law</th>\n",
       "      <th>Enforcement</th>\n",
       "      <th>Presence\",\"Law</th>\n",
       "      <th>Enforcement</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Were</th>\n",
       "      <th>Vehicles</th>\n",
       "      <th>Moved</th>\n",
       "      <th>Prior</th>\n",
       "      <th>To</th>\n",
       "      <th>Police</th>\n",
       "      <th>Arrival\",\"Trafficway</th>\n",
       "      <th>Ownership\",\"Trafficway</th>\n",
       "      <th>Ownership</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Trafficway</th>\n",
       "      <th>Class\",\"Trafficway</th>\n",
       "      <th>Class</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Police</th>\n",
       "      <th>Agency\",\"Police</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Badge</th>\n",
       "      <th>Number\",\"Approval</th>\n",
       "      <th>Date\",\"Police</th>\n",
       "      <th>Case</th>\n",
       "      <th>Status\",\"Police</th>\n",
       "      <th>Case</th>\n",
       "      <th>Status</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Crash</th>\n",
       "      <th>Report</th>\n",
       "      <th>Submission</th>\n",
       "      <th>Date\",\"Related</th>\n",
       "      <th>Case</th>\n",
       "      <th>Identifiers\",\"Urban</th>\n",
       "      <th>Area\",\"Urban</th>\n",
       "      <th>Area</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"No.</th>\n",
       "      <th>of</th>\n",
       "      <th>Through</th>\n",
       "      <th>Lanes</th>\n",
       "      <th>(Reverse</th>\n",
       "      <th>Dir.)\",\"No.</th>\n",
       "      <th>of</th>\n",
       "      <th>Through</th>\n",
       "      <th>Lanes</th>\n",
       "      <th>(Log</th>\n",
       "      <th>Dir.)\",\"Highway</th>\n",
       "      <th>Type</th>\n",
       "      <th>(FHWA)\",\"Highway</th>\n",
       "      <th>Type</th>\n",
       "      <th>(FHWA)</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Average</th>\n",
       "      <th>Daily</th>\n",
       "      <th>Traffic\",\"Functional</th>\n",
       "      <th>Classification\",\"Functional</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Text</th>\n",
       "      <th>Format\",\"Rural</th>\n",
       "      <th>or</th>\n",
       "      <th>Urban\",\"Rural</th>\n",
       "      <th>or</th>\n",
       "      <th>Urban</th>\n",
       "      <th>Text</th>\n",
       "      <td>Format\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777502,\"1\",\"Complete\",\"777502\",\"777502\",\"41.10104800\",\"-73.41696700\",\"3281612\",\"Norwalk\",\"103\",\"103\",\"Norwalk\",\"2021-01-03\",\"2021\",\"1\",\"January\",\"3\",\"1\",\"Sunday\",\"17:00:00\",\"17\",\"2100000489\",\"A\",\"Injury</th>\n",
       "      <th>of</th>\n",
       "      <th>any</th>\n",
       "      <th>type</th>\n",
       "      <th>(Serious,</th>\n",
       "      <th>Minor,</th>\n",
       "      <th>Possible)\",\"C\",\"Possible</th>\n",
       "      <th>Injury</th>\n",
       "      <th>(C)\",\"1\",\"1\",\"0\",\"4\",\"Local\",\"0.16\",\"0601\",\"NORTH</th>\n",
       "      <th>WATER</th>\n",
       "      <th>ST\",\"NORTH</th>\n",
       "      <th>WATER</th>\n",
       "      <th>ST\",\"MARSHALL</th>\n",
       "      <th>ST\",\"\",\"\",\"\",\"W\",\"41.10104800\",\"-73.41696700\",\"9\",\"Pedestrian\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"1\",\"On</th>\n",
       "      <th>Roadway\",\"4\",\"Rain\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"99\",\"Unknown\",\"99\",\"Unknown\",\"99\",\"Unknown\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"99\",\"Unknown\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"2\",\"Intersection\",\"3\",\"T-Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"true\",\"1\",\"Public</th>\n",
       "      <th>Road\",\"1\",\"Trafficway,</th>\n",
       "      <th>On</th>\n",
       "      <th>Road\",\"CT0010300\",\"Norwalk</th>\n",
       "      <th>PD\",\"B033\",\"2021-01-05</th>\n",
       "      <th>14:52:06.0\",\"O\",\"Open\",\"2021-01-05</th>\n",
       "      <th>14:59:44.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777511,\"1\",\"Complete\",\"777511\",\"777511\",\"41.18058100\",\"-73.15916300\",\"3281621\",\"Bridgeport\",\"15\",\"15\",\"Bridgeport\",\"2021-01-04\",\"2021\",\"1\",\"January\",\"4\",\"2\",\"Monday\",\"17:15:00\",\"17\",\"2100000988\",\"A\",\"Injury</th>\n",
       "      <th>of</th>\n",
       "      <th>any</th>\n",
       "      <th>type</th>\n",
       "      <th>(Serious,</th>\n",
       "      <th>Minor,</th>\n",
       "      <th>Possible)\",\"C\",\"Possible</th>\n",
       "      <th>Injury</th>\n",
       "      <th>(C)\",\"1\",\"1\",\"0\",\"3\",\"State\",\"5.42\",\"130\",\"130-E\",\"130-E\",\"DAVENPORT</th>\n",
       "      <th>ST\",\"\",\"\",\"\",\"\",\"41.18058100\",\"-73.15916300\",\"99\",\"Unknown\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"1\",\"On</th>\n",
       "      <th>Roadway\",\"1\",\"Clear\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"4\",\"Dark-Lighted\",\"1\",\"Dry\",\"99\",\"Unknown\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"99\",\"Unknown\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"2\",\"Intersection\",\"3\",\"T-Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"false\",\"1\",\"Public</th>\n",
       "      <th>Road\",\"1\",\"Trafficway,</th>\n",
       "      <th>On</th>\n",
       "      <th>Road\",\"CT0001509\",\"Bridgeport</th>\n",
       "      <th>PD\",\"215213\",\"2021-01-05</th>\n",
       "      <th>14:48:07.0\",\"O\",\"Open\",\"2021-01-05</th>\n",
       "      <th>15:20:09.0\",\"\",\"A\",\"BRIDGEPORT</th>\n",
       "      <th>-</th>\n",
       "      <th>STAMFORD\",\"2\",\"2\",\"4\",\"3-4</th>\n",
       "      <th>LANES</th>\n",
       "      <th>DIVIDED</th>\n",
       "      <th>(WIDTH</th>\n",
       "      <th>36'-71')\",\"14200\",\"4\",\"MINOR</th>\n",
       "      <th>ARTERIAL</th>\n",
       "      <th>-</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777818,\"1\",\"Complete\",\"777818\",\"777818\",\"41.69911700\",\"-72.89402500\",\"3281928\",\"Bristol\",\"17\",\"17\",\"Bristol\",\"2021-01-01\",\"2021\",\"1\",\"January\",\"1\",\"6\",\"Friday\",\"13:59:00\",\"13\",\"2100000052\",\"A\",\"Injury</th>\n",
       "      <th>of</th>\n",
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       "      <th>(Serious,</th>\n",
       "      <th>Minor,</th>\n",
       "      <th>Possible)\",\"C\",\"Possible</th>\n",
       "      <th>Injury</th>\n",
       "      <th>(C)\",\"1\",\"1\",\"0\",\"\",\"\",\"\",\"\",\"\",\"1400</th>\n",
       "      <th>Farmington</th>\n",
       "      <th>Avenue\",\"\",\"Camp</th>\n",
       "      <th>Street\",\"100.0\",\"Feet\",\"E\",\"41.69911700\",\"-72.89402500\",\"9\",\"Pedestrian\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"7\",\"In</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Lane</th>\n",
       "      <th>or</th>\n",
       "      <th>Zone\",\"1\",\"Clear\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"1\",\"Daylight\",\"1\",\"Dry\",\"0\",\"None\",\"88\",\"Not</th>\n",
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       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"97\",\"Other\",\"1\",\"Not</th>\n",
       "      <th>at</th>\n",
       "      <th>Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not</th>\n",
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       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"false\",\"2\",\"Private</th>\n",
       "      <th>Road\",\"4\",\"Parking</th>\n",
       "      <th>Lot\",\"CT0001700\",\"Bristol</th>\n",
       "      <th>PD\",\"P0210\",\"2021-01-07</th>\n",
       "      <th>03:30:24.0\",\"O\",\"Open\",\"2021-01-07</th>\n",
       "      <th>04:29:31.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777835,\"1\",\"Complete\",\"777835\",\"777835\",\"41.76178500\",\"-72.70672700\",\"3281945\",\"Hartford\",\"64\",\"64\",\"Hartford\",\"2021-01-04\",\"2021\",\"1\",\"January\",\"4\",\"2\",\"Monday\",\"19:14:00\",\"19\",\"21-000309\",\"A\",\"Injury</th>\n",
       "      <th>of</th>\n",
       "      <th>any</th>\n",
       "      <th>type</th>\n",
       "      <th>(Serious,</th>\n",
       "      <th>Minor,</th>\n",
       "      <th>Possible)\",\"C\",\"Possible</th>\n",
       "      <th>Injury</th>\n",
       "      <th>(C)\",\"1\",\"1\",\"0\",\"4\",\"Local\",\"0.35\",\"0473\",\"SISSON</th>\n",
       "      <th>AV\",\"SISSON</th>\n",
       "      <th>AV\",\"WEST</th>\n",
       "      <th>BOULEVARD\",\"\",\"\",\"\",\"\",\"41.76178500\",\"-72.70672700\",\"9\",\"Pedestrian\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"1\",\"On</th>\n",
       "      <th>Roadway\",\"1\",\"Clear\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"4\",\"Dark-Lighted\",\"1\",\"Dry\",\"0\",\"None\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"0\",\"None\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"2\",\"Intersection\",\"2\",\"Four-Way</th>\n",
       "      <th>Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"88\",\"Not</th>\n",
       "      <th>Applicable\",\"false\",\"1\",\"Public</th>\n",
       "      <th>Road\",\"1\",\"Trafficway,</th>\n",
       "      <th>On</th>\n",
       "      <th>Road\",\"CT0006400\",\"Hartford</th>\n",
       "      <th>PD\",\"G92\",\"2020-01-05</th>\n",
       "      <th>20:17:00.0\",\"C\",\"Closed\",\"2021-01-04</th>\n",
       "      <th>21:45:00.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                https://www.ctcrash.uconn.edu/QueryTool2.action?qid=175286\n",
       "CrashId,\"Fatal                                     Case Status\",\"Fatal Case Status    Text   Format\",\"CrashId\",\"CrashId\",\"Latitude\",\"Longitu... Case   Number\",\"Town                                      Name\",\"Town                                        Number\",\"Town       Number\",\"Town                                      Number                                             Text                                               Format\",\"Date                                      Of                            Crash\",\"Year\",\"Month\",\"Month                       Text                                               Format\",\"Day\",\"Day                           of                                        the                                                Week\",\"Day                        of                    the                                       Week                           Text                               Format\",\"Time                  of                               Crash\",\"Hour                         of                                              the                                             Day\",\"Case                         Identifier\",\"Crash                              Severity\",\"Crash Severity Text Format\",\"Most     Severe Injury\",\"Most Severe Injury Text Format\",\"Number Of  Motor Vehicles\",\"Number Of  Non-Motorist\",\"Number Of  Witnesses\",\"Route Class\",\"Route Class Text Format\",\"Milemarker\",\"Road Number\",\"Road Description\",\"Roadway Name\",\"Intersecting Roadway Name\",\"Landmark Description\",\"Distance From Nearest Landmark\",\"Unit Of  Measure From Nearest Landmark\",\"Direction From Nearest Landmark\",\"Latitude\",\"Longitude\",\"First Harmful Event\",\"First Harmful Event Text Format\",\"Manner of  Crash /   Collision Impact\",\"Manner of  Crash /   Collision Impact Text Format\",\"Location of  First Harmful Event\",\"Location of  First Harmful Event Text Format\",\"Weather Condition\",\"Weather Condition Text Format\",\"Weather 2\",\"Weather 2.0 Text Format\",\"Light Condition\",\"Light Condition Text Format\",\"Road Surface Condition\",\"Road Surface Condition Text Format\",\"Contributing Circumstances, Environment\",\"Contributing Circumstances, Environment Text Format\",\"Environment 2\",\"Environment 2.0 Text Format\",\"Environment 3\",\"Environment 3.0 Text Format\",\"Contributing Circumstances, Road\",\"Contributing Circumstances, Road Text Format\",\"Road 2\",\"Road 2.0 Text Format\",\"Road 3\",\"Road 3.0 Text Format\",\"Crash Specific Location\",\"Crash Specific Location Text Format\",\"Type of  Intersection\",\"Type of  Intersection Text Format\",\"School Bus Related\",\"School Bus Related Text Format\",\"Work Zone Related\",\"Work Zone Related Text Format\",\"Location Of  Crash Relative To  Work Zone\",\"Location Of  Crash Relative To  Work Zone Text Format\",\"Type of  Work Zone\",\"Type of  Work Zone Text Format\",\"Worker Presence\",\"Worker Presence Text Format\",\"Law Enforcement Presence\",\"Law Enforcement Presence Text Format\",\"Were Vehicles Moved Prior To  Police Arrival\",\"Trafficway Ownership\",\"Trafficway Ownership Text Format\",\"Trafficway Class\",\"Trafficway Class Text Format\",\"Police Agency\",\"Police Agency Text Format\",\"Badge Number\",\"Approval Date\",\"Police Case Status\",\"Police Case Status Text Format\",\"Crash Report Submission Date\",\"Related Case Identifiers\",\"Urban Area\",\"Urban Area Text Format\",\"No. of  Through Lanes (Reverse Dir.)\",\"No. of  Through Lanes (Log Dir.)\",\"Highway Type (FHWA)\",\"Highway Type (FHWA) Text Format\",\"Average Daily Traffic\",\"Functional Classification\",\"Functional Classification Text Format\",\"Rural or  Urban\",\"Rural or  Urban Text                                            Format\"        \n",
       "777502,\"1\",\"Complete\",\"777502\",\"777502\",\"41.101... of   any            type (Serious, Minor, Possible)\",\"C\",\"Possible                           Injury (C)\",\"1\",\"1\",\"0\",\"4\",\"Local\",\"0.16\",\"0601\",\"NORTH  WATER                                              ST\",\"NORTH          WATER                                              ST\",\"MARSHALL                                      ST\",\"\",\"\",\"\",\"W\",\"41.10104800\",\"-73.41696700\",\"... Applicable\",\"1\",\"On                                Roadway\",\"4\",\"Rain\",\"88\",\"Not Applicable\",\"99\",\"Unknown\",\"99\",\"Unknown\",\"99\",... Applicable\",\"88\",\"Not                              Applicable\",\"99\",\"Unknown\",\"88\",\"Not         Applicable\",\"88\",\"Not                     Applicable\",\"2\",\"Intersection\",\"3\",\"T-Intersect... Applicable\",\"88\",\"Not             Applicable\",\"88\",\"Not Applicable\",\"88\",\"Not                     Applicable\",\"true\",\"1\",\"Public Road\",\"1\",\"Trafficway,             On                             Road\",\"CT0010300\",\"Norwalk       PD\",\"B033\",\"2021-01-05               14:52:06.0\",\"O\",\"Open\",\"2021-01-05              14:59:44.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\" NaN                                NaN                                             NaN              NaN      NaN  NaN               NaN    NaN           NaN    NaN    NaN  NaN             NaN NaN   NaN               NaN NaN                   NaN NaN               NaN           NaN   NaN  NaN                        NaN           NaN                   NaN                 NaN     NaN             NaN                    NaN  NaN     NaN             NaN NaN     NaN  NaN     NaN                  NaN  NaN     NaN                                     NaN     NaN           NaN     NaN   NaN  NaN             NaN NaN   NaN NaN       NaN             NaN NaN   NaN NaN       NaN    NaN  NaN               NaN NaN   NaN     NaN              NaN NaN   NaN     NaN   NaN  NaN              NaN                 NaN       NaN  NaN              NaN         NaN NaN  NaN            NaN               NaN       NaN  NaN           NaN     NaN              NaN     NaN       NaN  NaN                   NaN            NaN                        NaN            NaN         NaN  NaN                  NaN             NaN NaN  NaN                  NaN             NaN NaN  NaN                   NaN            NaN                 NaN            NaN  NaN  NaN           NaN      NaN NaN  NaN           NaN      NaN NaN  NaN            NaN      NaN              NaN      NaN      NaN  NaN           NaN NaN                 NaN NaN          NaN  NaN             NaN NaN              NaN NaN     NaN  NaN           NaN  NaN            NaN  NaN     NaN  NaN               NaN NaN   NaN      NaN NaN  NaN             NaN NaN   NaN      NaN NaN  NaN  NaN  NaN           NaN NaN  NaN         NaN NaN  NaN  NaN  NaN             NaN               NaN      NaN  NaN          NaN         NaN            NaN         NaN      NaN  NaN           NaN      NaN   NaN   NaN NaN    NaN                  NaN                    NaN       NaN  NaN                 NaN                NaN   NaN  NaN             NaN             NaN    NaN  NaN            NaN               NaN           NaN  NaN             NaN  NaN    NaN  NaN            NaN    NaN        NaN            NaN  NaN                 NaN          NaN  NaN  NaN          NaN NaN     NaN   NaN      NaN         NaN NaN     NaN   NaN  NaN             NaN  NaN              NaN  NaN    NaN  NaN              NaN   NaN                  NaN                         NaN            NaN  NaN            NaN NaN           NaN NaN   NaN                                                 NaN        \n",
       "777511,\"1\",\"Complete\",\"777511\",\"777511\",\"41.180... of   any            type (Serious, Minor, Possible)\",\"C\",\"Possible                           Injury (C)\",\"1\",\"1\",\"0\",\"3\",\"State\",\"5.42\",\"130\",\"130-... ST\",\"\",\"\",\"\",\"\",\"41.18058100\",\"-73.15916300\",\"9... Applicable\",\"1\",\"On Roadway\",\"1\",\"Clear\",\"88\",\"Not                     Applicable\",\"4\",\"Dark-Lighted\",\"1\",\"Dry\",\"99\",\"... Applicable\",\"88\",\"Not                              Applicable\",\"99\",\"Unknown\",\"88\",\"Not               Applicable\",\"88\",\"Not         Applicable\",\"2\",\"Intersection\",\"3\",\"T-Intersect... Applicable\",\"88\",\"Not                              Applicable\",\"88\",\"Not                        Applicable\",\"88\",\"Not                     Applicable\",\"false\",\"1\",\"Public                    Road\",\"1\",\"Trafficway,            On                    Road\",\"CT0001509\",\"Bridgeport             PD\",\"215213\",\"2021-01-05       14:48:07.0\",\"O\",\"Open\",\"2021-01-05 15:20:09.0\",\"\",\"A\",\"BRIDGEPORT -                                STAMFORD\",\"2\",\"2\",\"4\",\"3-4           LANES                                           DIVIDED                                         (WIDTH                             36'-71')\",\"14200\",\"4\",\"MINOR                    ARTERIAL         -        NEW  HPMS\",\"2\",\"Urban\" NaN    NaN           NaN    NaN    NaN  NaN             NaN NaN   NaN               NaN NaN                   NaN NaN               NaN           NaN   NaN  NaN                        NaN           NaN                   NaN                 NaN     NaN             NaN                    NaN  NaN     NaN             NaN NaN     NaN  NaN     NaN                  NaN  NaN     NaN                                     NaN     NaN           NaN     NaN   NaN  NaN             NaN NaN   NaN NaN       NaN             NaN NaN   NaN NaN       NaN    NaN  NaN               NaN NaN   NaN     NaN              NaN NaN   NaN     NaN   NaN  NaN              NaN                 NaN       NaN  NaN              NaN         NaN NaN  NaN            NaN               NaN       NaN  NaN           NaN     NaN              NaN     NaN       NaN  NaN                   NaN            NaN                        NaN            NaN         NaN  NaN                  NaN             NaN NaN  NaN                  NaN             NaN NaN  NaN                   NaN            NaN                 NaN            NaN  NaN  NaN           NaN      NaN NaN  NaN           NaN      NaN NaN  NaN            NaN      NaN              NaN      NaN      NaN  NaN           NaN NaN                 NaN NaN          NaN  NaN             NaN NaN              NaN NaN     NaN  NaN           NaN  NaN            NaN  NaN     NaN  NaN               NaN NaN   NaN      NaN NaN  NaN             NaN NaN   NaN      NaN NaN  NaN  NaN  NaN           NaN NaN  NaN         NaN NaN  NaN  NaN  NaN             NaN               NaN      NaN  NaN          NaN         NaN            NaN         NaN      NaN  NaN           NaN      NaN   NaN   NaN NaN    NaN                  NaN                    NaN       NaN  NaN                 NaN                NaN   NaN  NaN             NaN             NaN    NaN  NaN            NaN               NaN           NaN  NaN             NaN  NaN    NaN  NaN            NaN    NaN        NaN            NaN  NaN                 NaN          NaN  NaN  NaN          NaN NaN     NaN   NaN      NaN         NaN NaN     NaN   NaN  NaN             NaN  NaN              NaN  NaN    NaN  NaN              NaN   NaN                  NaN                         NaN            NaN  NaN            NaN NaN           NaN NaN   NaN                                                 NaN        \n",
       "777818,\"1\",\"Complete\",\"777818\",\"777818\",\"41.699... of   any            type (Serious, Minor, Possible)\",\"C\",\"Possible                           Injury (C)\",\"1\",\"1\",\"0\",\"\",\"\",\"\",\"\",\"\",\"1400              Farmington                                         Avenue\",\"\",\"Camp    Street\",\"100.0\",\"Feet\",\"E\",\"41.69911700\",\"-72.8... Applicable\",\"7\",\"In                                Parking                                            Lane                                               or                            Zone\",\"1\",\"Clear\",\"88\",\"Not                        Applicable\",\"1\",\"Daylight\",\"1\",\"Dry\",\"0\",\"None\"... Applicable\",\"88\",\"Not                        Applicable\",\"0\",\"None\",\"88\",\"Not          Applicable\",\"88\",\"Not                              Applicable\",\"97\",\"Other\",\"1\",\"Not at                    Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not Applicable\",\"88\",\"Not          Applicable\",\"88\",\"Not              Applicable\",\"88\",\"Not          Applicable\",\"false\",\"2\",\"Private Road\",\"4\",\"Parking                   Lot\",\"CT0001700\",\"Bristol                       PD\",\"P0210\",\"2021-01-07                         03:30:24.0\",\"O\",\"Open\",\"2021-01-07 04:29:31.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\" NaN              NaN      NaN  NaN               NaN    NaN           NaN    NaN    NaN  NaN             NaN NaN   NaN               NaN NaN                   NaN NaN               NaN           NaN   NaN  NaN                        NaN           NaN                   NaN                 NaN     NaN             NaN                    NaN  NaN     NaN             NaN NaN     NaN  NaN     NaN                  NaN  NaN     NaN                                     NaN     NaN           NaN     NaN   NaN  NaN             NaN NaN   NaN NaN       NaN             NaN NaN   NaN NaN       NaN    NaN  NaN               NaN NaN   NaN     NaN              NaN NaN   NaN     NaN   NaN  NaN              NaN                 NaN       NaN  NaN              NaN         NaN NaN  NaN            NaN               NaN       NaN  NaN           NaN     NaN              NaN     NaN       NaN  NaN                   NaN            NaN                        NaN            NaN         NaN  NaN                  NaN             NaN NaN  NaN                  NaN             NaN NaN  NaN                   NaN            NaN                 NaN            NaN  NaN  NaN           NaN      NaN NaN  NaN           NaN      NaN NaN  NaN            NaN      NaN              NaN      NaN      NaN  NaN           NaN NaN                 NaN NaN          NaN  NaN             NaN NaN              NaN NaN     NaN  NaN           NaN  NaN            NaN  NaN     NaN  NaN               NaN NaN   NaN      NaN NaN  NaN             NaN NaN   NaN      NaN NaN  NaN  NaN  NaN           NaN NaN  NaN         NaN NaN  NaN  NaN  NaN             NaN               NaN      NaN  NaN          NaN         NaN            NaN         NaN      NaN  NaN           NaN      NaN   NaN   NaN NaN    NaN                  NaN                    NaN       NaN  NaN                 NaN                NaN   NaN  NaN             NaN             NaN    NaN  NaN            NaN               NaN           NaN  NaN             NaN  NaN    NaN  NaN            NaN    NaN        NaN            NaN  NaN                 NaN          NaN  NaN  NaN          NaN NaN     NaN   NaN      NaN         NaN NaN     NaN   NaN  NaN             NaN  NaN              NaN  NaN    NaN  NaN              NaN   NaN                  NaN                         NaN            NaN  NaN            NaN NaN           NaN NaN   NaN                                                 NaN        \n",
       "777835,\"1\",\"Complete\",\"777835\",\"777835\",\"41.761... of   any            type (Serious, Minor, Possible)\",\"C\",\"Possible                           Injury (C)\",\"1\",\"1\",\"0\",\"4\",\"Local\",\"0.35\",\"0473\",\"SISSON AV\",\"SISSON                                        AV\",\"WEST           BOULEVARD\",\"\",\"\",\"\",\"\",\"41.76178500\",\"-72.70672... Applicable\",\"1\",\"On                                Roadway\",\"1\",\"Clear\",\"88\",\"Not                     Applicable\",\"4\",\"Dark-Lighted\",\"1\",\"Dry\",\"0\",\"N... Applicable\",\"88\",\"Not         Applicable\",\"0\",\"None\",\"88\",\"Not                   Applicable\",\"88\",\"Not                              Applicable\",\"2\",\"Intersection\",\"2\",\"Four-Way Intersection\",\"1\",\"No\",\"1\",\"No\",\"88\",\"Not Applicable\",\"88\",\"Not                              Applicable\",\"88\",\"Not             Applicable\",\"88\",\"Not Applicable\",\"false\",\"1\",\"Public           Road\",\"1\",\"Trafficway,         On                                 Road\",\"CT0006400\",\"Hartford    PD\",\"G92\",\"2020-01-05            20:17:00.0\",\"C\",\"Closed\",\"2021-01-04 21:45:00.0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\" NaN                                             NaN                                NaN                                             NaN              NaN      NaN  NaN               NaN    NaN           NaN    NaN    NaN  NaN             NaN NaN   NaN               NaN NaN                   NaN NaN               NaN           NaN   NaN  NaN                        NaN           NaN                   NaN                 NaN     NaN             NaN                    NaN  NaN     NaN             NaN NaN     NaN  NaN     NaN                  NaN  NaN     NaN                                     NaN     NaN           NaN     NaN   NaN  NaN             NaN NaN   NaN NaN       NaN             NaN NaN   NaN NaN       NaN    NaN  NaN               NaN NaN   NaN     NaN              NaN NaN   NaN     NaN   NaN  NaN              NaN                 NaN       NaN  NaN              NaN         NaN NaN  NaN            NaN               NaN       NaN  NaN           NaN     NaN              NaN     NaN       NaN  NaN                   NaN            NaN                        NaN            NaN         NaN  NaN                  NaN             NaN NaN  NaN                  NaN             NaN NaN  NaN                   NaN            NaN                 NaN            NaN  NaN  NaN           NaN      NaN NaN  NaN           NaN      NaN NaN  NaN            NaN      NaN              NaN      NaN      NaN  NaN           NaN NaN                 NaN NaN          NaN  NaN             NaN NaN              NaN NaN     NaN  NaN           NaN  NaN            NaN  NaN     NaN  NaN               NaN NaN   NaN      NaN NaN  NaN             NaN NaN   NaN      NaN NaN  NaN  NaN  NaN           NaN NaN  NaN         NaN NaN  NaN  NaN  NaN             NaN               NaN      NaN  NaN          NaN         NaN            NaN         NaN      NaN  NaN           NaN      NaN   NaN   NaN NaN    NaN                  NaN                    NaN       NaN  NaN                 NaN                NaN   NaN  NaN             NaN             NaN    NaN  NaN            NaN               NaN           NaN  NaN             NaN  NaN    NaN  NaN            NaN    NaN        NaN            NaN  NaN                 NaN          NaN  NaN  NaN          NaN NaN     NaN   NaN      NaN         NaN NaN     NaN   NaN  NaN             NaN  NaN              NaN  NaN    NaN  NaN              NaN   NaN                  NaN                         NaN            NaN  NaN            NaN NaN           NaN NaN   NaN                                                 NaN        "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv(local_file_path)\n",
    "df = pd.read_csv(local_file_path, delimiter=\" \", quotechar='\"')\n",
    "# Remove the web link from the first row (URL)\n",
    "df = df.iloc[0:]  # Skip the first row\n",
    "\n",
    "# Display the cleaned DataFrame\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/home/yul23028/CTI_LLM/llama.cpp/models/Llama-2-7B-GGUF/llama-2-7b.Q3_K_S.gguf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.callbacks.manager import CallbackManager\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain_community.llms import LlamaCpp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's work this out in a step by step way to be sure we have the right answer.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(template)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks support token-wise streaming\n",
    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "langchain_llm_venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
